Despite ongoing prevention campaigns, the HIV epidemic persists on a global scale. To monitor the effects of intervention campaigns, and more generally, to estimate incidence and prevalence in human populations, patterns of disease spread must be accurately reconstructed. Our project brings together a team of experienced researchers from clinical, molecular biology, epidemiological, mathematical, and evolutionary fields. We will carefully examine phylogenetic limitations and develop statistical methods to address and quantify their effects on transmission reconstruction. We will build novel phylodynamic inference methods that adequately take these limitations into account, and incorporate recent statistical advances developed in social network and epidemiological sciences. We will use seven large datasets describing both HIV within-host and between-host evolution and transmission. These data comprise many thousands of HIV cases from the US, Europe, former Soviet Union, and Africa, described by HIV sequence, clinical and demographic data. The overarching hypothesis of this project is that the evolutionary process of HIV-1 records genetic mutations affected by its epidemiological history. Thus, in this project we aim to extract epidemiological information from phylogenetic analyses of HIV and integrate it with clinical, demographical, and geographical data to push the boundaries of current state-of-the-art epidemiological inference methods. Our specific aims are: 1) Develop a more realistic within-host model of HIV evolutionary dynamics; 2) Jointly infer the unobserved transmission history and virus phylogeny; and 3) Create public software resources that facilitate molecular epidemiology inferences.